Semnan University Press Journal of Modeling in Engineering 2008-4854 22 79 2024 12 21 DFDA-AD: An Approach with Dual Feature Extraction Architecture and Dual Attention Mechanism for Image Anomaly Detection DFDA-AD: An Approach with Dual Feature Extraction Architecture and Dual Attention Mechanism for Image Anomaly Detection 45 57 9172 10.22075/jme.2024.31105.2484 FA Babak Masoudi Department of Information Technology, Payamenoor University (PNU), P.O.Box, 19395-3697 Tehran, I.R of Iran Journal Article 2023 07 01 Detecting and locating unwanted structures or anomalies in the image is one of the important issues in machine vision and industrial inspection. The complexity and variability of data distribution and the lack of labeled data are among the challenges of detecting anomalies in images. In recent years, deep learning methods have provided promising results for solving anomaly detection problems in any data types, especially in images. In this paper, the DFDA-AD architecture, which is an unsupervised approach based on deep learning, is proposed for anomaly detection in industrial images. DFDA-AD consists of dual feature extraction from images by pre-trained DenseNet121 and ResNet50 networks. Two attention mechanisms are improved and developed in this paper, which provide more important feature maps for clustering by K-means algorithm. The evaluation of the model's performance was done on the MVTec AD data set, and the results of the evaluations for anomaly detection and localization were satisfactory compared to several other approaches that have been recently proposed.<br />  Detecting and locating unwanted structures or anomalies in the image is one of the important issues in machine vision and industrial inspection. The complexity and variability of data distribution and the lack of labeled data are among the challenges of detecting anomalies in images. In recent years, deep learning methods have provided promising results for solving anomaly detection problems in any data types, especially in images. In this paper, the DFDA-AD architecture, which is an unsupervised approach based on deep learning, is proposed for anomaly detection in industrial images. DFDA-AD consists of dual feature extraction from images by pre-trained DenseNet121 and ResNet50 networks. Two attention mechanisms are improved and developed in this paper, which provide more important feature maps for clustering by K-means algorithm. The evaluation of the model's performance was done on the MVTec AD data set, and the results of the evaluations for anomaly detection and localization were satisfactory compared to several other approaches that have been recently proposed.<br />  https://modelling.semnan.ac.ir/article_9172_5e0763eb489e8328b2d7bd4639777686.pdf